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Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner
Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner
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- More about Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner
This book aims to provide a comprehensive guide to machine learning for non-technical audiences,using practical implementations and real datasets. It covers the data mining process,presentation of results,and is designed for beginners and experienced practitioners.
Format: Hardback
Length: 577 pages
Publication date: 23 February 2024
Publisher: Taylor & Francis Ltd
The importance of data science and machine learning has surged significantly as data continues to expand at an exponential rate. While machine learning has experienced remarkable growth, the vast array of resources available can be overwhelming for newcomers. This book seeks to bridge this gap and cater to learners from diverse non-technical backgrounds, enabling them to effectively harness the power of machine learning.
Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are consistently employed, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also offers detailed guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.
Designed as a comprehensive guide for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds. By delving into the fundamentals of machine learning and providing practical applications, this book equips readers with the skills and knowledge needed to thrive in the rapidly evolving field of data science and machine learning.
Weight: 1250g
Dimension: 254 x 178 (mm)
ISBN-13: 9780367755386
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